The present invention pertains to detection systems and methods. More particularly, the present invention pertains to detection systems and methods using the near-infrared spectrum for the detection of, for example, facial features.
In many situations, detection of individuals is very important. For example, detection of individuals is required in many high-end security applications, e.g., surveillance of an embassy perimeter where there is a need to know who certain individuals are within a particular setting. Further, for example, such detection systems and methods may not only be required in high-end security situations, but may also be needed in government buildings, schools, airports, and border control points. As such, systems for detection and identification of individuals, e.g., detection at a distance, need to be developed and implemented. Such systems would be most advantageous in the context of protecting high value assets (e.g. perimeter of government buildings) from asymmetric (e.g., terrorist) threats.
Generally, certain recent biometric technologies (e.g., such as face recognition systems that may be able to match pre-stored data regarding a particular individual to real time collected data of an individual) have been developed which may be used in situations such as those described above. However, such face recognition systems are not without problems. For example, many face recognition systems do not have adequate detection techniques for detecting that one or more persons exist within a scene being monitored (e.g., identification of the existence of a person and/or detection of one or more facial features of a person). Further, for example, many face recognition systems are not able to successfully determine the orientation of a person's face or extent of a person's face such that a face recognition algorithm can be effectively applied to the detected individual.
Face detection, e.g., the detection of a face in a scene and/or the detection of one or more facial features of a person in a scene, is an important prerequisite step for successful application of face recognition algorithms. For example, stored facial signatures must be applied to a facial image in coordinated or aligned fashion. In other words, face detection is an important preprocessing stage of an overall face recognition system and provides the face recognition system with one or more points or facial features that allow the stored facial signature of individuals to be compared effectively to a current image of a person that has been detected. For example, the face detection technique may provide a location of the center of the eyes on an image of an individual such that the facial signature can be aligned therewith (e.g., alignment of the eyes of facial signatures with the eyes of the image of the individual being analyzed).
Face detection is a challenging machine vision operation, particularly in outdoor environments where illumination varies greatly. Such environmental conditions is one of the primary reasons that face recognition systems are generally constrained to access control applications in indoor settings. Therefore, a major technical challenge that needs to be addressed in expanding the use of such face recognition technologies is the low performance of face detectors in unconstrained environments.
Visible-band face detectors, such as those reported in the literature, opt for pure algorithmic solutions to inherent phenomenology problems. Human facial signatures vary significantly across races in the visible band. This variability coupled with dynamic lighting conditions present a formidable problem. Reducing light variability through the use of an artificial illuminator is rather awkward in the visible band because it may be distracting to the eyes of the people in the scene and “advertises” the existence of the surveillance system.
In recent years a sizable body of research in the area of face detection has been amassed. The methodologies vary, but the research mainly centers around three different approaches: artificial neural networks, feature extraction, and wavelet analysis. Each of these approaches has its respective strengths and weaknesses when applied to face detection, but none has yet been able to attain results rivaling human perception.
The majority of face detection research has been focused around various types of feature extraction. Feature extraction methods utilize various properties of the face and skin to isolate and extract desired data. Popular methods include skin color segmentation, principal component analysis, Eigenspace modeling, histogram analysis, texture analysis, and frequency domain features.
Face detection research based on artificial neural networks has also received attention. One of the problems with this approach is finding a representative data set. This difficulty is compounded by the fact that a strong counter example set must also be compiled to train the individual networks.
The general aim of the wavelet approach is maximum class discrimination and signal dimensionality reduction. Due to the reduced dimensionality, wavelet-based methods are computationally efficient.
However, all of the above approaches are associated with visible spectrum imagery. Therefore, they are susceptible to light changes and the variability of human facial appearance in the visible band.